title: Sewer deterioration modeling with condition data lacking historical records.
abstract: Accurate predictions of future conditions of sewer systems are needed for efficient rehabilitation planning. For this purpose, a range of sewer deterioration models has been proposed which can be improved by calibration with observed sewer condition data. However, if datasets lack historical records, calibration requires a combination of deterioration and sewer rehabilitation models, as the current state of the sewer network reflects the combined effect of both processes. Otherwise, physical sewer lifespans are overestimated as pipes in poor condition that were rehabilitated are no longer represented in the dataset. We therefore propose the combination of a sewer deterioration model with a simple rehabilitation model which can be calibrated with datasets lacking historical information. We use Bayesian inference for parameter estimation due to the limited information content of the data and limited identifiability of the model parameters. A sensitivity analysis gives an insight into the model's robustness against the uncertainty of the prior. The analysis reveals that the model results are principally sensitive to the means of the priors of specific model parameters, which should therefore be elicited with care. The importance sampling technique applied for the sensitivity analysis permitted efficient implementation for regional sensitivity analysis with reasonable computational outlay. Application of the combined model with both simulated and real data shows that it effectively compensates for the bias induced by a lack of historical data. Thus, the novel approach makes it possible to calibrate sewer pipe deterioration models even when historical condition records are lacking. Since at least some prior knowledge of the model parameters is available, the strength of Bayesian inference is particularly evident in the case of small datasets.
authors.affiliation: Eawag, Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, P.O. Box 611, CH-8600 Dübendorf, Switzerland; ETH Zürich, Department of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering, Wolfgang-Pauli-Strasse 15, CH-8093 Zürich, Switzerland. Electronic address: christoph.egger@eawag.ch.
authors.forename: C
authors.initials: C
authors.lastname: Egger
authors.forename: A
authors.initials: A
authors.lastname: Scheidegger
authors.forename: P
authors.initials: P
authors.lastname: Reichert
authors.forename: M
authors.initials: M
authors.lastname: Maurer
mesh_terms: D002138:Calibration; D016013:Likelihood Functions; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method; D012498:Sanitary Engineering; D012722:Sewage
chemical_list: D012722:Sewage
keywords: Bayesian inference; Data management; Deterioration model; Likelihood; Rehabilitation model; Survival selection bias